A Complete Real-Time Feature Extraction and Matching System Based on Semantic Kernels Binarized
Abstract
Feature extraction and matching is an important step in many current image and video processing algorithms. In this work, we designed and implemented an efficient feature extraction and matching system for sparse point correspondence search in stereo video. Our system is based on the recently proposed Semantic Kernels Binarized (SKB) algorithm, which showed superior performance with respect to other algorithms in our evaluation. The feature extraction stage has been prototyped in 180 nm technology and the complete system with two feature extraction pipelines (left and right view) together with the matching unit have been implemented on a Stratix IV FPGA where it delivers a performance of up to 42 frames per second on 720p video. Especially due to the high throughput of up to 25 k matched descriptors per frame, our system compares favourably with recent hardware implementations of similar algorithms.
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